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A platform for research: civil engineering, architecture and urbanism
Multi-Stage Robust Scheduling for Community Microgrid with Energy Storage
Energy storage devices can effectively balance the uncertain load and significantly reduce electricity costs in the community microgrids (C-MGs) integrated with renewable energy sources. Scheduling of energy storage is a multi-stage decision problem in which the decisions must be guaranteed to be nonanticipative and multi-stage robust (all-scenario-feasible). To satisfy these two requirements, this paper proposes a method based on a necessary and sufficient feasibility condition of scheduling decisions under the polyhedral uncertainty set. Unlike the most popular affine decision rule (ADR) based multi-stage robust optimization (MSRO) method, the method proposed in this paper does not require the affine decision assumption, and the feasible regions (the set of all feasible solutions) are not reduced, nor is the solution quality affected. A simple illustrative example and real-scale scheduling cases demonstrate that the proposed method can find feasible solutions when the ADR-based MSRO fails, and that it finds better solutions when both methods succeed. Comprehensive case studies for a real system are performed and the results validate the effectiveness and efficiency of the proposed method.
Multi-Stage Robust Scheduling for Community Microgrid with Energy Storage
Energy storage devices can effectively balance the uncertain load and significantly reduce electricity costs in the community microgrids (C-MGs) integrated with renewable energy sources. Scheduling of energy storage is a multi-stage decision problem in which the decisions must be guaranteed to be nonanticipative and multi-stage robust (all-scenario-feasible). To satisfy these two requirements, this paper proposes a method based on a necessary and sufficient feasibility condition of scheduling decisions under the polyhedral uncertainty set. Unlike the most popular affine decision rule (ADR) based multi-stage robust optimization (MSRO) method, the method proposed in this paper does not require the affine decision assumption, and the feasible regions (the set of all feasible solutions) are not reduced, nor is the solution quality affected. A simple illustrative example and real-scale scheduling cases demonstrate that the proposed method can find feasible solutions when the ADR-based MSRO fails, and that it finds better solutions when both methods succeed. Comprehensive case studies for a real system are performed and the results validate the effectiveness and efficiency of the proposed method.
Multi-Stage Robust Scheduling for Community Microgrid with Energy Storage
Ye Tang (author) / Qiaozhu Zhai (author) / Jiexing Zhao (author)
2023
Article (Journal)
Electronic Resource
Unknown
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